Intelligent video thumbnail selection and generation

ABSTRACT

In accordance with one embodiment, an intelligent video thumbnail selection and generation tool may select a relevant and visually stimulating image from a video file and generate a thumbnail including the image. The image may be selected by computing a relevancy metric for an image in the file based on one or more selected relevant features, and comparing that relevancy metric with the metric of at least one other image in the file. In another embodiment, a series of images in a video file may be divided into shots. One of the shots may be selected based on a shot relevancy metric and a key image from the shot may be selected as a thumbnail based on a key image relevancy metric, where the shot relevancy metric and the key image relevancy metrics may be computed based on one or more relevant content features.

This application claims the benefit under 35 U.S.C. §119(e) of U.S.provisional patent applications 61/712,067 filed on Oct. 10, 2012 and61/778,569 filed on Mar. 13, 2013 each of which is hereby incorporatedby reference in its entirety and for all purposes.

BACKGROUND

Video sharing websites such as YouTube.com™ facilitate user searches forvideo content and make a large number of videos viewable by the public.When a user performs a keyword search for video content, the user may beoffered a series of thumbnail images representative of videos returnedin the search. High-traffic video sharing websites maintainuser-searchable indices and may add hundreds or even thousands ofadditional videos every day. Therefore, one challenge that websitemanagers face is the need to quickly generate thumbnail imagesrepresentative of each new video uploaded. Increased viewership ofcertain videos is desirable, in particular, by advertisers that displayadvertisements alongside videos or before, during, or after a video isplayed. Therefore, another challenge is ensuring that the thumbnailswill be both visually stimulating and representative of the content inthe underlying video so that the user will be encouraged to click on thethumbnail and view the associated video.

SUMMARY

Embodiments described herein may be utilized to address at least one ofthe foregoing problems by providing systems and methods for quicklyselecting and generating thumbnail images that are relevant and visuallystimulating. In one embodiment, a relevancy metric is computed for animage in a video file based on a selection of content features and arepresentative image is selected as a thumbnail based on a comparisonbetween the relevancy metric of the image and the relevancy metric of atleast one other image in the video file. In another embodiment, a seriesof images in a video file are divided into shots. One of the shots isselected based on a shot relevancy metric and an image from the shot isselected as a thumbnail based on an image relevancy metric, where theshot relevancy metric and the image relevancy metric are computed basedon one or more features of the content in each image or shot.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presenttechnology may be realized by reference to the figures, which aredescribed in the remaining portion of the specification.

FIG. 1 is an example of a user interface for selecting thumbnail imagefor a video, in accordance with one embodiment.

FIG. 2 is a flow chart illustrating a method of processing an input filein order to generate a representative image, in accordance with oneembodiment.

FIG. 3 is an example operation 100 for selecting and generating relevantand visually stimulating thumbnails.

FIG. 4 is a flow chart illustrating a method of selecting a relevantimage to represent a collection of images, such as a video, inaccordance with one embodiment.

FIG. 5 is another flow chart illustrating a method of selecting arelevant image to represent a collection of images, such as a video, inaccordance with one embodiment.

FIG. 6 is a block diagram illustrating a system for implementing athumbnail selection process, in accordance with one embodiment.

FIG. 7 illustrates an example computer system that may be useful inimplementing the described technology.

DETAILED DESCRIPTION

Online video content providers of high-traffic video-sharing websitesseek to quickly generate thumbnail images representative of the videocontent in each of a number of different videos. For some websites it isalso important that the representative thumbnail images be visuallyexciting and/or stimulating to entice viewers to click on videossponsored by various advertisers. However, automated thumbnailgeneration processes do not always guarantee that the thumbnail imagesselected to represent a video will be either visually stimulating orparticularly representative of the corresponding video content. Indeed,some online video distribution systems merely generate thumbnails fromrandom portions of the video, such as at the 25%, 50% , and 75% mark inthe video. A choice is then made from one of the three randomthumbnails. This rigid and non-intelligent method for selecting thethumbnails cannot guarantee that any of the proposed thumbnails will bevisually pleasing or a good representation of the content. Moreover, itcan be costly and time-consuming to require content providers or contentmanagers to hand-select and, if needed, modify thumbnail images.Therefore, in accordance with one embodiment, a tool may be utilizedthat selects and generates a representative thumbnail image for a videothat is both relevant and visually stimulating.

A thumbnail selection and generation tool can intelligently select athumbnail image for a video file by computing a relevancy metric for oneor more frames in the video based on features indicative of whether theframe image is visually stimulating and relevant to the correspondingvideo file. A representative frame is selected as the thumbnail imagebased on a comparison between the relevancy metric of one or moreadditional frames.

An image is visually stimulating if it temporarily increaseselectrochemical activity in the brain. For example, an image is visuallystimulating if it arouses or holds the viewer's attention, sparkscuriosity, or creates interest or excitement. Accordingly, a thumbnailimage may be considered visually stimulating if it contains brightcolors or significant contrast, texture, complex or unusual shapes,living subjects, etc.

In accordance with one embodiment, a thumbnail is considered to be agood and effective thumbnail if it meets one or more of the followingcriteria:

-   -   Clear, in-focus, hi-resolution    -   Bright, high contrast    -   Close-ups of faces (if relevant to the content)    -   Visually-compelling imagery    -   Well-framed, good composition    -   Foreground stands out from background    -   Looks great at both small and large sizes    -   Accurately represents the content

A thumbnail image is relevant to a corresponding video file when theimage is representative of the subject matter or subjects featured inthe video. For instance, a dark frame at the beginning of a scene isneither relevant nor visually stimulating. However, a frame in a cookingvideo featuring a chef in a kitchen wearing a tall white chef's hat isrelevant because it indicates the subject matter of the video. Thisimage may be considered visually stimulating because it includes aliving subject and recognizable objects; however, the image may beconsidered even more visually stimulating if, for example, the image isa clear close-up of the chef's face and the viewer is able to observe afacial expression conveying emotion.

Throughout this disclosure, the term “frame” refers to not only a framewithin a motion picture or animation file, but also a slide within apresentation file (e.g., a PowerPoint slide) where the file may or maynot have an accompanying audio track. A thumbnail image is an imagerepresentative of a corresponding file, wherein the thumbnail is basedon at least one frame of the file. A thumbnail can be of any size andmay contain text, graphics, or additional enhancements not originallypresent in the frame that is selected as the thumbnail image. In atleast one embodiment, the thumbnail image may contain text or graphics.The term “image” used herein is understood to include a “frame.”

FIG. 1 illustrates an example of a user interface for selecting a morepreferred thumbnail image. In FIG. 1 a group of images for a particularvideo file are displayed. The image in block 104 is the default imagethat was assigned to the video. The default image could be assigned in avariety of ways. For example, it could be the thumbnail image that wasoriginally provided by a video distribution site, such as YouTube. Afterprocessing the video, a group of alternative thumbnail images can besuggested. Five of these suggested images are shown to the right ofblock 106 along with the original image. The five suggested imagesprovide potentially better alternatives than the original image for thepurpose of being relevant and of interest to a potential user. Forexample, the original image in block 104 shows the singer but her faceis obscured by a tree. The other images present the singer's face morevisibly. In the example of FIG. 1, the arrow indicates that the userselects the image designated as block 106. This image presents a clearview of the singer's face and is a larger facial image than the otherimages. However, the user has discretion as to which of the five imagesto select. Once the user selects an image to be a thumbnail, that imageis moved to block 104 and the user interface can present a button thatallow the user to confirm selection as a thumbnail. Once the userselects the button, the thumbnail can be transferred to the videodistribution site for use as a thumbnail image for the associated video.

FIG. 2 illustrates an implementation for selecting a thumbnail imagethat is representative of content. In operation 206, a content providerprovides content that is received as an input. This content can be avideo file, audio file, text, or PowerPoint presentation, for example.For purposes of this example, a video file will be used as the example.In operation 208, the video file is pre-processed. The pre-processingoperation can include resizing and filtering. In operation 210, aselection of the best video frames is automatically made. The proposedbest video frames can then be manually refined by the content provider.In operation 212, post-processing of the best video frames isimplemented. The post-processing operation can include, for example,image resizing, cropping, and image enhancement. In operation 214, text,graphics or image (such as logo) insertion can be implemented. Operation216 illustrates that thumbnails may then be output.

In one embodiment, video and audio pre-processing is performed prior tothumbnail image selection. Video processing methods are oftencomputationally expensive. Pre-processing can reduce a portion of thatcomputational expense. The video and audio pre-processing may includetemporal down sampling and frame size reduction. Digital videos mayoffer many frames per second so temporal down sampling can eliminateredundancies without losing relevant visual information. For example, inorder to create the illusion of movement, digital videos frequentlyoffer about 15 to 30 frames for every second. Therefore, there is someredundancy in adjacent video frames. That redundancy provides theopportunity to drop a large amount of frames without losing any relevantvisual information.

In the same or a different embodiment, the frames are downsized,preserving the original aspect ratio. Downsizing may be desirable toreduce the total number of computations necessary to complete theintelligent thumbnail selection and generation process. It should benoted that other solutions for reducing the amount of computation mightalso be employed. Some examples include combining a number of successiveframes using some weighting function or reducing the number of framesper second using a nonlinear frame-reduction function.

FIG. 3 is an embodiment of an example operation 300 for selecting arepresentative thumbnail image for a video. The video is a file havingany frame rate, aspect ratio, size, or length that may or may not havean audio track. The video may be any of a number of file typesincluding, but not limited to, MPEG, MPEG2, H.264, HEVC, FLV, SWF, AVI,MP4, WMV, etc.

Content creators can also specify what category the video belongs to asan input. Examples of the categories include but are not limited to:

-   -   Movie, movie trailer or movie clip    -   Video games    -   Animation    -   Music video    -   Game play    -   Vlog (Video blog)    -   Sports clip    -   News clip

A selection operation 302 selects one or more content features relatingto the video file to be used in the thumbnail image selection operation300. A content feature is a feature that allows a frame or group offrames to be evaluated in a meaningful way that assists in the selectionof relevant and visually stimulating thumbnails. For example, a contentfeature may be: contrast, brightness, texture, sharpness, cameramovement, the presence of “skin-colored” pixels, the number of facespresent, the number of open eyes, whether the eyes are open or closed,the size of faces or other human features visible, text or other objectsthat are identifiable in each frame, etc.

In one embodiment, a content feature is contrast. It is believed thatviewers of videos respond better to high contrast images. Therefore,frames with high contrast are often preferred.

Image brightness can also be a feature. In one embodiment, very dark orvery bright images are avoided.

In one embodiment, a content feature is camera movement. One can betterunderstand the visual quality of a frame by understanding how it fits inrelationship with its adjacent frames. One way of achieving this isthrough using the camera movements. For example, if the camera iszooming in, zooming out, or moving from left to right or from top tobottom, then the image is more likely to be blurry. Therefore, framesexhibiting camera movement may not be desirable as thumbnails.

A zooming in feature identifies if a frame is part of a shot where thecamera is zooming in. As an example, frames with camera movement mighthave a lower ranking than frames that belong to a steady shot.

A zooming out feature identifies if a frame is part of a shot where thecamera is zooming out. As an example, frames with camera movement mighthave a lower ranking than frames that belong to a steady shot.

A panning feature identifies if a frame is part of a series of frameswhere the camera is panning, e.g., moving from left to right or fromright to left. Frames that fit into this category are likely to beblurry and therefore, would have a lower ranking than frames coming froma steady shot.

A tilting feature identifies if a frame is part of a series of frameswhere the camera is tilting, e.g., moving from top to bottom or frombottom to top. Frames that fit into this category are likely to beblurry and therefore, should have a lower ranking than frames comingfrom a steady shot.

Image texture can also be a content feature. Images containing a lot oftexture tend to be visually stimulating and may thus be desirable asthumbnails. In one embodiment, an image texture value is arrived at bycalculating the standard deviation for every channel in a frame.Examples of color channels include but are not limited to RGB (Red;Green; Blue), YCbCr (Luminance; Chroma: Blue; Chroma: Red) and HSV (Hue;Saturation ;Value). Standard deviations for each channel are averaged toobtain a single standard deviation value for each frame. In anotherembodiment, an image texture value is calculated by dividing a colorframe into three channels such as Y, Cb and Cr and calculating thestandard deviation of the Y component for each frame. A higher averagestandard deviation is indicative of more texture in an image, so a framefeaturing many objects, colors and shapes will have a higher channelstandard deviation than an image of a green wall or a blue sky. In otherembodiments, alternate methods of calculating texture may be employed.

Images containing live entities such as animals and especially peopleare more likely to be relevant and visually stimulating than other typeof images. Thus, the number of pixels in a frame that are “skin-colored”may also be a content feature indicative of the visual appeal andrelevance of a particular image. In an embodiment utilizing a“skin-color” relevance feature, a range of pixel color values may bedefined as “skin-colored.” A pixel falling within this range does notnecessarily represent skin, but a large number of such pixels may tendto indicate that it is more likely the frame includes people and istherefore relevant and visually stimulating.

A shot having one or more human faces is also likely to be visuallystimulating and relevant. Thus, the number of human faces in every frameor shot may also be a content feature. The number of human faces may bedetected by employing one or more machine-learning modules designed toextract and recognize faces from cluttered shots. Methods of detectingfaces can be implemented, for example, by the following steps:segmentation of faces (face detection) from cluttered screens, featureextraction from the face regions, and recognition or verification.

Thumbnail images with close-ups of people may be more likely to capturea viewer's attention than shots containing dozens of people. Forinstance, an image including face that is large compared to the size ofthe frame may be more desirable as a thumbnail than a small face if aviewer is able to readily identify an interesting facial expression.Thus, the size of faces visible in a frame may be a content feature. Inone implementation, the size of the largest face on a frame is estimatedand used as content feature.

An image with the subject's eyes open may be more appealing than onewith the eyes closed. Therefore, the number of eyes or open eyes may bea content feature indicative of a visually stimulating, relevant image.The number of open eyes in an image may be determined by employing anumber of methods including but not limited to pattern, color matching,or object recognition processes.

In alternate embodiments, relevant features chosen may include human andanimal features besides skin color, such as the number of human oranimal heads, arms, legs, etc. Such features may be identified by, forexample, employing one or more machine-learning modules, such as anobject recognition library. Other embodiments may utilize machinelearning modules to calculate, for example, specific objects, events,and places.

In alternate embodiments, one or more composition-based features areselected as the content features. For instance, a content feature may bewhether a region of interest in the frame is located near the center ofthe frame. Popular online video distribution systems such as YouTubefeature a triangular “play icon” in the center of the thumbnail.Therefore, it may be desirable to have thumbnails that do not have aprimary region of interest in the center of the frame where it is likelyto be covered up. A number of methods may be employed to determinewhether an object of interest is located in the center of a frame. As anexample, in one embodiment, a saliency map can be used. The saliency mapmay indicate the importance of every pixel on the image. One example ofa saliency map is a gray scale 2D array with the same dimensions as asource image. A saliency map is usually computed by employing some imagefeatures such as color, brightness and edges. If the images are videoframes, then motion can also be used as a saliency indicator.

Another composition-based feature is how balanced an image is. Manyvisual artists use the “rule of thirds” as a guideline for creating apleasing, balanced composition. The objective is to locate the region ofinterest near one of the lines that divides the image into three equalcolumns and rows. It can then be determined whether the frame followsthe “rule of thirds” or not. Similarly, the “rule of odds” states thatby framing the object of interest with an even number of surroundingobjects, the framed object becomes more comforting to the eye. The “ruleof odds” thus creates a feeling of ease and pleasure.

In another embodiment, the blurriness or depth of field is a contentfeature. For example, if the foreground of an image is sharp and thebackground is blurry, then the image may have a small depth of field.Therefore, it may be desirable to select a representative thumbnailimage having a larger depth of field. However, it should be appreciatedthat images that feature a small depth of field may occasionally makegood thumbnails since this implies that the foreground is isolated fromthe background. For instance, a clear image of a face against a blurrybackground might make an appealing thumbnail. Therefore, in an alternateembodiment, priority may be given to certain images having a small depthof field, especially when the sharp region of the frame includes anobject of interest (e.g., a face).

In one embodiment, the sharpness of an image is computed by computingthe edges and counting the number of pixels that are part of these edgesas well as their magnitudes. Moreover, a video frame can be divided intoa number of blocks (e.g., four). Each block can be measured forsharpness. If at least one of the blocks is sharper than a predeterminedthreshold, then the image is considered to be sharp. For the case offrames featuring faces, the sharpness detection process can be applied,for example, to the region with the largest face.

In another embodiment, text is a content feature. An optical characterrecognition module (OCR) may be utilized to extract text from selectedframes of a video file, and the extracted text may be utilized inselecting and generating a thumbnail image. For example, an OCR mayparse a presentation file for slides containing the title of thepresentation. One or more frames containing the title may be given moreweight in the thumbnail image selection process.

In yet another embodiment, audio feature information may be utilized asa content feature in selecting the best potential thumbnails from avideo file. For example, if higher volume or higher intensity music isidentified in a portion of a video relative to other portion(s) of thevideo, then the video frames associated with that music are more likelyto be exciting and thus visually stimulating. Likewise, speechrecognition modules may be utilized alone or in conjunction with facialrecognition modules to determine whether an image should be selected asa thumbnail.

One embodiment utilizes a database with faces of famous people orcharacters including politicians, athletes, cartoon characters,celebrities, and movie stars. The database may be employed to determinewhether a video features any famous people or characters that are in thedatabase. If so, one or more images featuring such persons or charactersmay be recommended as a thumbnail image.

Another embodiment utilizes a database with famous scenes and objectsincluding famous buildings, landmarks, or brands. The database may beemployed to determine whether a video features any famous building,landmark, brand, object, etc. that are in the database. If so, one ormore images featuring such buildings, landmarks, brands, or objects maybe recommended as a thumbnail image.

In FIG. 3, a division operation 304 divides the video into differentshots (also referred to herein as scenes), 306, 308, and 310, where ashot is a sequence of frames captured by a single continuous operationof the camera. Shot boundaries may be identified by detecting changes inspatial continuity. For example, a significant difference in the colorhistogram of two adjacent frames may indicate a shot boundary.

In one embodiment, shot boundaries are determined by comparing colorhistograms of adjacent frames. In an alternate embodiment, each frame isdivided into different color channels (e.g., R, G, and B in RGB space)in a specific color space such as RGB. Histograms of each channel arecomputed and averaged for each frame. The average histogram for eachframe is then correlated with the average histogram from the previousframe. If the correlation value computed for two given frames is lowerthan a predetermined threshold value then it will be assumed that thetwo frames are from two different shots.

Referring back to FIG. 3, the shot boundaries may therefore beidentified by comparing average color histograms of each of frames 312,314, 316, 318, 320, 322, 324, 326, and 328 with adjacent frames. Forexample, the histogram of frame 310 may be compared to frame 312.Because both shots capture similar scenery, the average color histogramsare closely correlated. However, when the average histogram of frame 324is correlated with the average histogram of frame 326, the correlationvalue is much lower because the two frames capture very differentimagery. Thus, the correlation value falls below a predeterminedthreshold value and the frames are partitioned into two different shots,308 and 310 respectively.

In one embodiment, the threshold value may be dynamically altered ifvery few or only one shot is initially found. If it is determined thatthere is only one actual shot in a video file, artificial shots may becreated by dynamically lowering the threshold value so that the singleshot can be divided into two or more shots. The “threshold shot length”,which determines the number of shots available, may be a function of thenumber of thumbnails ultimately desired. In one embodiment, thethreshold shot length is set to ensure that the number of shots (N) isat least twice the number of thumbnails that are desired. Thisembodiment may be desirable because it permits thumbnail image selectionfor single-shot videos.

In an alternate embodiment where the video file contains only one shot,the video is not partitioned into artificial shots. Rather, thethumbnail generation process begins at computation operation 334, wherea frame relevancy metric is computed for each frame.

In yet another embodiment where there are multiple shots in a video, thevideo is not divided into shots. Rather, a frame metric is computed foreach frame or a selection of frames and a representative frame isselected based on the frame metric.

A computation operation 330 computes a shot metric for each shot basedon one or more of the selected content features. The shot metric is avalue that represents the overall relevancy and visual appeal of eachshot. For each of the content features selected, a representativenumerical value may be computed and factored into the shot metriccomputation.

In one embodiment, the average value of every feature is computed usingall of the frames in the shot (e.g., average texture value for frames inthe shot, average number of skin-colored pixels for frames in the shot,etc.). Once the average content feature values are calculated for eachshot, the average values are normalized based on the maximum averagecontent features in all shots. A shot relevancy metric is computed basedon the normalized average content feature values for each shot.

In another embodiment, the median value of every feature is computedusing all of the frames in the shot. Once the median feature values arecalculated for each shot, they are normalized based on the maximummedian value of the features in all shots. A shot relevancy metric iscomputed based on the normalized values of content features for eachshot.

In another embodiment, the shot relevancy metric is based on a weightedsum of values representing the content feature values for each frame.The weighted sum is multiplied by the length of the shot (the number offrames in the shot) to obtain the shot relevancy metric. Longer shotsmay be given more weight than shorter shots because they tend toindicate relevance from a storytelling point of view. For instance, therelevancy metric may be:

shot relevancy=length×Σ_(n=1) ^(F) w _(n) f _(n),   (1)

where f_(n) is a content feature value and ω_(n) is a weight assigned tothe n^(th) content feature. Each of these weights can be empiricallydetermined or defined based on the nature of the content. As an example,in one embodiment, it can be determined that faces and eyes are moreimportant than texture and contrast. In another embodiment, the shotrelevancy metric can be defined as follows:

$\begin{matrix}{{{shot}\mspace{14mu} {relevancy}} = {\sum\limits_{n = 1}^{F}\; {\omega_{n}f_{n}}}} & (2)\end{matrix}$

where f_(n) and ω_(n) are defined as above in Equation 1. In oneembodiment that utilizes this metric, a default weight is assigned toeach of the content features. In an alternate embodiment, a useruploading a video selects a category relating to the video (e.g.,movies, games, music, animation, etc.) and weights are assigned to eachof the content features based on the category selected. For example, ifthe video is in an “Animation” category, there is no reason to giveweight to the number of skin pixels in each frame, but it may bedesirable to give more weight to brightness or contrast. For animationvideos, skin colored pixels are not important since many animatedcharacters are not supposed to be human. Animation videos usuallyfeature animals, moving objects, monsters and aliens. However, mostanimated characters do possess human-like features and face detectioncan be useful.

In an embodiment utilizing the exemplary shot relevancy metric shown inEquation 1, the shot with the highest metric is deemed to be “the bestshot.” In the same or an alternate embodiment, more than one shot isselected.

In another embodiment, a content provider uploading a video is asked toclassify the video as a video game, movie, movie trailer, non-profit,etc. When the classification selected is related to a narrative such asa movie trailer, music video, or video game trailer, then the length ofthe shot may be given additional weight in computing the shot relevancymetric.

A selection operation 332 selects a best shot based on the shotrelevancy metric. More than one best shot may be chosen if more than onethumbnail is desired. Computational operation 332 computes a framerelevancy metric for each frame of the one or more best shots selected.The frame relevancy metric is a value computed for each frame within thebest shot(s) selected that is representative of the relevancy and visualappeal of each individual frame. The frame relevancy metric for eachframe may take into account, for example, one or more of the same,similar, or additional content features as those discussed above incalculating the shot relevancy metric, such as the average texture valueof a shot, the average number of “skin-colored” pixels per frame in theshot, the number of faces present the shot, the number of eyes presentin the shot, whether the eyes are open or closed, or the size of thefaces or other human features visible in each shot.

Additionally, the frame metric may take into account the location ofeach particular frame within the shot. There is a higher probabilitythat frames taken at the beginning or at the end of shots will betransition frames and include information from more than one shot, suchas fade-ins, fade-outs, swipes, etc. Therefore, it may be desirable tochoose a representative thumbnail close to the center of the shot. Thus,in one embodiment, frame location is a content feature that is givenmore weight in the frame relevancy computation when the frame is closerto the center of the shot.

A selection operation 336 selects one or more of the best representativeframes from the winning shot(s). The one or more representative framesselected may be recommended or automatically selected as thumbnailimages. In one embodiment, a number of recommended thumbnail images arepresented to a user and the user selects one image to be the thumbnail.In another embodiment, one or more recommended thumbnails areautomatically associated with the video file and selected as thumbnails.

Selected frames may be subject to additional post-processing. In oneembodiment, during a post-processing operation, thumbnail images can becropped to improve their composition or in another embodiment, they canbe enhanced for greater contrast or brightness. In an alternateembodiment, text and/or graphics are added to the final thumbnailimages. Another example of enhancing the quality of the selected frameis to add an image to it. In one embodiment, an image can automaticallybe added to the thumbnail. An example of such an image is the company orthe brand's logo or a logo displaying the word “HD” (which stands forhigh-definition if the video is in high definition). Such logos can comein different sizes and depending on the complexity of the image, adifferent size might be chosen. In one embodiment, the texture on everycorner of each frame is calculated. Then the region with less texture isdetermined. Based on this information, the size and the location of theHD logo can be automatically adjusted. In yet another embodiment,instead of the logo, some text provided as an input by the user can beadded to the selected frame. Font restrictions may be placed on the textto ensure that text added in the final thumbnail image is large enoughto be read. In an alternate embodiment, added graphics and/or text maybe enhanced in color, resized, or otherwise altered prior to inclusionin the final thumbnail image.

The resulting frames after applying the above operations are thenpresented to the content creator as possible thumbnails. The contentcreator can then choose the thumbnail that he or she believes is thebest. The default setting of the process might be such that the topranked frame is automatically selected as the best thumbnail and theprocess of generating the best thumbnail is done without theintervention of the user.

In one embodiment, the operation 300 for selecting and generatingrelevant and visually stimulating thumbnails is integrated into a systemfor creating, optimizing, deploying, and analyzing content on the web.The system may be an online system accessible on the web or a softwaremodule, which can be downloaded and/or installed on a personal computer.This system, hereinafter referred to as the “platform” may allow a userto perform a number of operations, such as create or edit a video file;optimize metadata (e.g., titles, descriptions, and tags); create arelevant and visually stimulating thumbnail; deploy the video to one ormore video-sharing or social media websites; and provide detailedanalytics highlighting the performance of a video or a group of videos.In one embodiment, the platform may contain one or more tools allowing auser to edit or change an audio track in a file. It may also have one ormore tools that enable the user to delete or rearrange shots or frameswithin the file. In another embodiment, the platform may have a metadatarecommendation tool (e.g., for recommending title, description andkeywords) that suggests keywords for the user to include when sharingthe video on a public website such as YouTube, which prompts contentproviders to enhance the metadata of their videos.

FIG. 4 illustrates an example of selecting an image to represent acollection of images, such as a video. In operation 402, a relevancymetric for a first target image is obtained. The first target image canbe an image selected from a collection of images, such as an image froma video. The relevancy metric can be obtained by applying one or morerelevancy conditions to the target image so as to generate a score forthe image. This process can be repeated for at least a second targetimage. In operation 404, the relevancy metric of the first target imagecan be compared with the relevancy metric of the second target image.The comparison operation determines the target image with the higherrelevance, based on the compared relevancy metrics. The target imagewith the higher relevance is then transmitted for presentation through auser interface. For example, the more relevant image is uploaded to avideo distribution site, such as YouTube, for use as a thumbnail.

A relevancy metric can be based on one or more content features. Arelevancy metric may also indicate the relevancy of an image to thesubject matter of a collection of images. In one embodiment, therelevancy metric may include a visual stimulation metric. In anotherembodiment, the relevancy metric may be based on at least one of anumber of faces in a targeted image, a size of a face in a targetedimage, a number of eyes in a targeted image, and/or a number ofskin-colored pixels in a targeted image. Moreover, a relevancy metricmay be based on content located in an off-center position in a targetedimage.

The collection of images described in FIG. 4 may be processed by downsampling, downsizing, and/or filtering. Moreover, text, graphics, and/oran additional image may be added to a targeted image.

FIG. 5 illustrates another example of selecting an image to represent acollection of images. In operation 502, a collection of images isdivided into two or more subgroups. Each subgroup contains its ownrespective set of subgroup images. In operation 504, one of thesubgroups is selected based on a visual similarity metric. In operation506, one image from the selected subgroup is selected to be a selectedimage based on a relevancy metric.

The visual similarity metric can be based on a number of images in aselected subgroup. Moreover, the visual similarity metric can be basedon at least one of a number of faces in an image, a size of a face in animage, a number of eyes in an image, and a number of skin-colored pixelsin an image. Also, the selected image may be resized or filtered.

FIG. 6 illustrates an example of a system that can be utilized toimplement various embodiments discussed herein. FIG. 6 shows a usercomputer 604. The user computer 604 has a user interface that allows theuser to access video content and select thumbnail images for the videocontent. The interface also allows the user to add text or an image(e.g., logo) to a thumbnail. Moreover, the user computer allows the userto upload data to a video distribution site, such as YouTube, forexample. A second computer is shown in this example as computer 608. Inthis example, computer 608 is used to process a video in order togenerate a more relevant thumbnail image than might originally beprovided with a video. Computer 616 is shown having a user interface616. The computer is shown having some exemplary tools that can be usedto process a video as described herein. Computer tool 610 can besoftware run on computer 608 that allows the computer to downsample aselected video. Computer tool 612 can be software run on computer 608that allows the computer to downsize a selected video. And, computertool 614 can be software run on the computer 608 that allows thecomputer to filter the video. Computer 608 can be utilized by thecontent provider himself/herself, by a commercial entity separate fromthe content provider, or by a video distribution site, for example. Inthis example, a separate entity that is separate from the contentprovider and video distribution site uses computer 608.

The content provider can select a video (where a video falls into thecategory of a collection of images) that is stored locally at thecontent provider's computer or on a remote database 620. Database 622can be utilized to store software tools that set conditions, such asrelevancy conditions, for a targeted image. For example, database 622can store a computer tool in the form of software that analyzes how manyskin tone pixels appear in a targeted image of a video. Database 624represents a database that stores content features such as images offamous people for comparison to images in a video, for example.

Computers 616 and 618 represent different video distribution sites. Oneexample of a video distribution site is YouTube. Videos can be uploadedto a video distribution site once a thumbnail has been selected inaccordance with the methods described herein. Alternatively, a videodistribution site might choose to download a video to a third partyservice provider using computer 608 in order to have a thumbnailselected for the video.

In another example, computer 608 may also be configured to divide agroup of images into a plurality of subgroups. Each subgroup can includemore than one subgroup images, for example arranged in chronologicalorder. At least one image from the selected subgroup can be selectedbased on a relevancy metric.

The visual similarity metric can be based on a number of images in aselected subgroup. Moreover, the visual similarity metric can be basedon at least one of a number of faces in an image, a size of a face in animage, a number of eyes in an image, and a number of skin-colored pixelsin an image. Also, the selected image may be resized or filtered.

FIG. 7 discloses a block diagram of a computer system 700 suitable forimplementing aspects of at least one embodiment. The computer system 700may be used to implement one or more components of the intelligentthumbnail generation tool disclosed herein. For example, in oneembodiment, the computer system 700 may be used to implement each of theserver 702, the client computer 708, and the intelligent thumbnailselection and generation tool stored in an internal memory 706 or aremovable memory 722. As shown in FIG. 7, system 700 includes a bus 702which interconnects major subsystems such as a processor 704, internalmemory 706 (such as a RAM or ROM), an input/output (I/O) controller 708,removable memory (such as a memory card) 722, an external device such asa display screen 710 via a display adapter 712, a roller-type inputdevice 714, a joystick 716, a numeric keyboard 718, an alphanumerickeyboard 720, smart card acceptance device 724, a wireless interface726, and a power supply 728. Many other devices can be connected.Wireless interface 726 together with a wired network interface (notshown), may be used to interface to a local or wide area network (suchas the Internet) using any network interface system known to thoseskilled in the art.

Many other devices or subsystems (not shown) may be connected in asimilar manner. Also, it is not necessary for all of the devices shownin FIG. 7 to be present to practice an embodiment. Furthermore, thedevices and subsystems may be interconnected in different ways from thatshown in FIG. 7. Code to implement one embodiment may be operablydisposed in the internal memory 706 or stored on storage media such asthe removable memory 727, a floppy disk, a thumb drive, a CompactFlash®storage device, a DVD-R (“Digital Versatile Disc” or “Digital VideoDisc” recordable), a DVD-ROM (“Digital Versatile Disc” or “Digital VideoDisc” read-only memory), a CD-R (Compact Disc-Recordable), or a CD-ROM(Compact Disc read-only memory). For example, in an embodiment of thecomputer system 700, code for implementing the intelligent thumbnailselection and generation tool may be stored in the internal memory 706and configured to be operated by the processor 704.

In the above description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments described. It will be apparent,however, to one skilled in the art that these embodiments may bepracticed without some of these specific details. For example, whilevarious features are ascribed to particular embodiments, it should beappreciated that the features described with respect to one embodimentmay be incorporated with other embodiments as well. By the same token,however, no single feature or features of any described embodimentshould be considered essential, as other embodiments may omit suchfeatures.

In the interest of clarity, not all of the routine functions of theembodiments described herein are shown and described. It will, ofcourse, be appreciated that in the development of any such actualembodiment, numerous implementation-specific decisions must be made inorder to achieve the developer's specific goals, such as compliance withapplication—and business-related constraints, and that those specificgoals will vary from one embodiment to another and from one developer toanother.

According to one embodiment, the components, process steps, and/or datastructures disclosed herein may be implemented using various types ofoperating systems (OS), computing platforms, firmware, computerprograms, computer languages, and/or general-purpose machines. Themethod can be run as a programmed process running on processingcircuitry. The processing circuitry can take the form of numerouscombinations of processors and operating systems, connections andnetworks, data stores, or a stand-alone device. The process can beimplemented as instructions executed by such hardware, hardware alone,or any combination thereof The software may be stored on a programstorage device readable by a machine.

According to one embodiment, the components, processes and/or datastructures may be implemented using machine language, assembler, C orC++, Java and/or other high level language programs running on a dataprocessing computer such as a personal computer, workstation computer,mainframe computer, or high performance server running an OS such asSolaris® available from Sun Microsystems, Inc. of Santa Clara, Calif.,Windows 8, Windows 7, Windows Vista™, Windows NT®, Windows XP PRO, andWindows® 2000, available from Microsoft Corporation of Redmond, Wash.,Apple OS X-based systems, available from Apple Inc. of Cupertino,Calif., BlackBerry OS, available from Blackberry Inc. of Waterloo,Ontario, Android, available from Google Inc. of Mountain View, Calif. orvarious versions of the Unix operating system such as Linux availablefrom a number of vendors. The method may also be implemented on amultiple-processor system, or in a computing environment includingvarious peripherals such as input devices, output devices, displays,pointing devices, memories, storage devices, media interfaces fortransferring data to and from the processor(s), and the like. Inaddition, such a computer system or computing environment may benetworked locally, or over the Internet or other networks. Differentimplementations may be used and may include other types of operatingsystems, computing platforms, computer programs, firmware, computerlanguages and/or general purpose machines; and. In addition, those ofordinary skill in the art will recognize that devices of a less generalpurpose nature, such as hardwired devices, field programmable gatearrays (FPGAs), application specific integrated circuits (ASICs), or thelike, may also be used without departing from the scope and spirit ofthe inventive concepts disclosed herein.

The above specification, examples, and data provide a completedescription of the structure and use of exemplary embodiments of theinvention. Since many implementations of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims hereinafter appended. Furthermore,structural features of the different implementations may be combined inyet another implementation without departing from the recited claims.

What is claimed is:
 1. A method comprising: obtaining a relevancy metricfor a first targeted image of a collection of images; comparing with acomputer the relevancy metric of the first targeted image with arelevancy metric of at least a second targeted image of the collectionof images; transmitting for presentation through a user interface theimage having the higher relevancy metric.
 2. The method of claim 1wherein the relevancy metric for the first targeted image is based onone or more content features.
 3. The method of claim 1 wherein therelevancy metric for the first targeted image indicates a relevancy ofthe first targeted image to the subject matter of the collection ofimages.
 4. The method of claim 1 wherein the relevancy metric for thefirst targeted image comprises a visual stimulation metric.
 5. Themethod of claim 1 and further comprising at least one of downsizing,down sampling, or filtering the collection of images.
 6. The method ofclaim 1 wherein the relevancy metric is based on at least one of anumber of faces in the first targeted image, a size of a face in thefirst targeted image, a number of eyes in the first targeted image, or anumber of skin-colored pixels in the first targeted image.
 7. The methodof claim 1, wherein the relevancy metric is based on at least one oftexture of the first targeted image, brightness of the first targetedimage, or contrast of the first targeted image.
 8. The method of claim 1wherein the relevancy metric for the first targeted image is based oncontent located in an off-center position in the first targeted image.9. The method of claim 1 and further comprising adding text, graphics,or an additional image to a selected image.
 10. A method comprising:dividing a group of images into a plurality of subgroups, each subgroupcomprising a plurality of subgroup images arranged in chronologicalorder; selecting one of the plurality of subgroups based on a visualsimilarity metric; and selecting at least one image from the selectedsubgroup to be a selected image based on a relevancy metric.
 11. Themethod of claim 10, wherein the visual similarity metric is based on anumber of images in the selected subgroup.
 12. The method of claim 10,wherein the visual similarity metric is based on at least one of anumber of faces in an image, a size of a face in an image, a number ofeyes in an image, a number of “skin-colored” pixels in an image, texturein an image, brightness in an image, and contrast of an image.
 13. Themethod of claim 10 wherein the relevancy metric is based on contentlocated in an off-center position in an image.
 14. The method of claim10 and further comprising adding text, graphics, or an additional imageto the selected image.
 15. The method of claim 10, further comprising atleast one of resizing or filtering the selected image.
 16. A systemcomprising: a relevancy metric for a first targeted image of acollection of images; a computer configured to compare the relevancymetric of the first targeted image with a relevancy metric of at least asecond targeted image of the collection of images; a user interfaceconfigured to transmit for presentation the image having the higherrelevancy metric.
 17. The system of claim 16 wherein the relevancymetric for the first targeted image is based on one or more contentfeatures.
 18. The system of claim 16 wherein the relevancy metric forthe first targeted image indicates a relevancy of the first targetedimage to the subject matter of the collection of images.
 19. The systemof claim 16 wherein the relevancy metric for the first targeted imagecomprises a visual stimulation metric.
 20. The system of claim 16wherein the computer is configured at least to downsize, down sample, orfilter the collection of images.
 21. The system of claim 16 wherein therelevancy metric is based on at least one of a number of faces in thefirst targeted image, a size of a face in the first targeted image, anumber of eyes in the first targeted image, and a number of skin-coloredpixels in the first targeted image.
 22. The system of claim 16, whereinthe relevancy metric is based on at least one of texture of the firsttargeted image, brightness of the first targeted image, or contrast ofthe first targeted image.
 23. The system of claim 16 wherein therelevancy metric for the first targeted image is based on contentlocated in an off-center position in the first targeted image.
 24. Thesystem of claim 16, further comprising adding text, graphics, or anadditional image to a selected image.
 25. A system comprising: a visualsimilarity metric; a computer configured to: divide a group of imagesinto a plurality of subgroups, each subgroup comprising a plurality ofsubgroup images arranged in chronological order; select one of theplurality of subgroups based on a visual similarity metric; and selectat least one image from the selected subgroup to be a selected imagebased on a relevancy metric.
 26. The system of claim 25, wherein thevisual similarity metric is based on a number of images in the selectedsubgroup.
 27. The system of claim 25, wherein the visual similaritymetric is based on at least one of a number of faces in an image, a sizeof a face in an image, a number of eyes in an image, a number of“skin-colored” pixels in an image, texture in an image, brightness in animage, and contrast of an image.
 28. The system of claim 25 wherein therelevancy metric is based on content located in an off-center positionin an image.
 29. The system of claim 25 wherein the computer is furtherconfigured to add text, graphics, or an additional image to the selectedimage.
 30. The system of claim 25, wherein the computer is furtherconfigured to perform at least one of resizing or filtering the selectedimage.
 31. One or more computer-readable storage media encodingcomputer-executable instructions for executing on a computer system acomputer process, the computer process comprising: obtaining a relevancymetric for a first targeted image of a collection of images; comparingwith a computer the relevancy metric of the first targeted image with arelevancy metric of at least a second targeted image of the collectionof images; transmitting for presentation through a user interface theimage having the higher relevancy metric.
 32. One or morecomputer-readable storage media encoding computer-executableinstructions for executing on a computer system a computer process, thecomputer process comprising: dividing a group of images into a pluralityof subgroups, each subgroup comprising a plurality of subgroup imagesarranged in chronological order; selecting one of the plurality ofsubgroups based on a visual similarity metric; and selecting at leastone image from the selected subgroup to be a selected image based on arelevancy metric.